Streamlining patient care with NLP

Our client, the leading healthcare service provider in the southwestern US, serves a large patient network and generates vast amounts of records on a daily basis. The company faced significant challenges in managing its large volumes of patient data which hindered the extraction of critical insights and promoted the creation of dark data.

Challenges included:

• Siloed patient information. Patient data was spread across electronic health records (EHRs), physician notes, lab reports, and medical images, making it difficult for healthcare providers to get a complete picture of a patient’s health.
• Data overload. Furthermore, a significant number of records were located in narrative documents, such as physicians notes and reports. As a result, healthcare professionals spent significant time manually reviewing and summarizing patient data, reducing time available for care and increasing the possibility of human error.
• Inefficient information search. Searching for specific patient information was time-consuming and often led to incomplete results, impacting diagnosis and treatment decisions.

To bridge the gap between raw text from various sources and organized data, the client sought a solution to transform raw patient data into actionable insights. Recognizing our many years of expertise in developing artificial intelligence (AI)-driven solutions and our dedicated R&D department, we were chosen to address the client’s challenges.

We developed a comprehensive patient data platform integrated with the existing EHR infrastructure. The platform leverages AI and natural language processing (NLP) technologies to enable:

Legacy data integration

Optical character recognition (OCR) digitized historical data from paper charts and handwritten notes. NLP then helped extract key information, enriching patient records and providing important historical context for healthcare professionals.

Integration of medical imaging

Using advanced computer vision and image recognition algorithms, we successfully extracted data from X-rays, CT scans, and other diagnostic images. This data was integrated into the patient records, improving access to critical information at the point of care.

Intuitive semantic search

The NLP-powered semantic search engine we developed overcomes the limitations of traditional keyword-based systems. Clinicians can now perform natural language searches and retrieve the most relevant information, regardless of how it is documented.

Single patient profile

We implemented a robust entity resolution system that combines NLP with rule-based matching to create a single, comprehensive profile, so-called “golden record” for each patient. This involved:

• Data cleansing and standardization. NLP algorithms, alongside rule-based systems, standardized formatting, abbreviations, and terminology, ensuring data consistency and interpretability.
• Entity resolution. Advanced NLP techniques and probabilistic matching algorithms identified and linked records belonging to the same patient, even with variations in names or identifiers.

To achieve these goals, we implemented the following AI and NLP-based techniques:

• OCR
• NLP (Named entity recognition (NER), text cassification, entity resolution)
• Computer vision and image recognition

The implementation of EffectiveSoft’s advanced solution delivered tangible results for the client, significantly improving patient care and operational efficiency. Healthcare professionals now have faster access to comprehensive and organized patient information, enabling more accurate diagnoses and timely treatments. The structured and digitized patient data serves as a valuable resource for generating data-driven insights and supporting personalized treatment strategies, enhancing the client’s healthcare operations.